{"id":"W3012523934","doi":"10.1049/iet-ipr.2019.1029","title":"Background subtraction using infinite asymmetric Gaussian mixture models with simultaneous feature selection","year":2020,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Background subtraction; Subtraction; Pattern recognition (psychology); Gaussian; Feature selection; Mixture model; Computer science; Selection (genetic algorithm); Artificial intelligence; Feature (linguistics); Gaussian process; Mathematics; Algorithm; Physics; Arithmetic","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002323155,0.001600228,0.001797062,0.001541356,0.0005647361,0.001445684,0.002411281,0.001495755,0.001113667],"category_scores_gemma":[0.003715019,0.0009437213,0.002208358,0.001657838,0.0007835247,0.002131043,0.002013238,0.002193717,0.00090059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007756075,"about_ca_system_score_gemma":0.001026268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006524264,"about_ca_topic_score_gemma":0.007784962,"domain_scores_codex":[0.9986092,0.0004809925,0.00006084404,0.0003312451,0.0003911129,0.0001266008],"domain_scores_gemma":[0.9988534,0.0005885121,0.00008749063,0.0001959647,0.0002266092,0.00004795952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004829871,0.0002238984,0.001690213,0.000198697,0.0004246356,0.0001461318,0.0002565893,0.349817,0.03636361,0.03027899,0.003700876,0.5764164],"study_design_scores_gemma":[0.0000118958,0.00002275974,0.0002270932,0.000005693827,0.00002085076,0.00003771386,0.000009686929,0.9858456,0.005164057,0.007616037,0.001023077,0.00001550282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003518072,0.0001430278,0.99563,0.00005778729,0.00001295826,0.00001363095,0.00002161801,0.0003283616,0.0002746098],"genre_scores_gemma":[0.229077,0.0005539306,0.7652717,0.0002510217,0.00008990338,0.0001201501,0.0006149399,0.0003801504,0.003641234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006524264,"threshold_uncertainty_score":0.01297259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03128967769602271,"score_gpt":0.283177917989747,"score_spread":0.2518882402937243,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}